Simulation 1

Column

Model dimensionality when noise \(\sim N(\mu = 0, \sigma^2 = 0.1)\).

Model dimensionality when noise \(\sim N(\mu = 0, \sigma^2 = 1)\).

Model dimensionality when noise \(\sim N(\mu = 0, \sigma^2 = 10)\).

Column

AUC when noise \(\sim N(\mu = 0, \sigma^2 = 0.1)\).

AUC when noise \(\sim N(\mu = 0, \sigma^2 = 1)\).

AUC when noise \(\sim N(\mu = 0, \sigma^2 = 10)\).

Simulation 2

Column

Model dimensionality when \(P=2\).

Model dimensionality when \(P=10\).

Model dimensionality when \(P=100\).

Model dimensionality when \(P=1000\).

Column

AUC when \(P=2\).

AUC when \(P=10\).

AUC when \(P=100\).

AUC when \(P=1000\).

Simulation 3

Column

Model dimensionality when \(P=2\) and noise \(\sim N(\mu = 0, \sigma^2 = 0.1)\).

Model dimensionality when \(P=2\) and noise \(\sim N(\mu = 0, \sigma^2 = 1)\).

Model dimensionality when \(P=2\) and noise \(\sim N(\mu = 0, \sigma^2 = 10)\).

Model dimensionality when \(P=10\) and noise \(\sim N(\mu = 0, \sigma^2 = 0.1)\).

Model dimensionality when \(P=10\) and noise \(\sim N(\mu = 0, \sigma^2 = 1)\).

Model dimensionality when \(P=10\) and noise \(\sim N(\mu = 0, \sigma^2 = 10)\).

Model dimensionality when \(P=100\) and noise \(\sim N(\mu = 0, \sigma^2 = 0.1)\).

Model dimensionality when \(P=100\) and noise \(\sim N(\mu = 0, \sigma^2 = 1)\).

Model dimensionality when \(P=100\) and noise \(\sim N(\mu = 0, \sigma^2 = 10)\).

Model dimensionality when \(P=1000\) and noise \(\sim N(\mu = 0, \sigma^2 = 0.1)\).

Model dimensionality when \(P=1000\) and noise \(\sim N(\mu = 0, \sigma^2 = 1)\).

Model dimensionality when \(P=1000\) and noise \(\sim N(\mu = 0, \sigma^2 = 10)\).

Column

AUC when \(P=2\) and noise \(\sim N(\mu = 0, \sigma^2 = 0.1)\).

AUC when \(P=2\) and noise \(\sim N(\mu = 0, \sigma^2 = 1)\).

AUC when \(P=2\) and noise \(\sim N(\mu = 0, \sigma^2 = 10)\).

AUC when \(P=10\) and noise \(\sim N(\mu = 0, \sigma^2 = 0.1)\).

AUC when \(P=10\) and noise \(\sim N(\mu = 0, \sigma^2 = 1)\).

AUC when \(P=10\) and noise \(\sim N(\mu = 0, \sigma^2 = 10)\).

AUC when \(P=100\) and noise \(\sim N(\mu = 0, \sigma^2 = 0.1)\).

AUC when \(P=100\) and noise \(\sim N(\mu = 0, \sigma^2 = 1)\).

AUC when \(P=100\) and noise \(\sim N(\mu = 0, \sigma^2 = 10)\).

AUC when \(P=1000\) and noise \(\sim N(\mu = 0, \sigma^2 = 0.1)\).

AUC when \(P=1000\) and noise \(\sim N(\mu = 0, \sigma^2 = 1)\).

AUC when \(P=1000\) and noise \(\sim N(\mu = 0, \sigma^2 = 10)\).

Simulation 4

Column

Model dimensionality when \(P=2\) and noise \(\sim N(\mu = 0, \sigma^2 = 0.1)\).

Model dimensionality when \(P=2\) and noise \(\sim N(\mu = 0, \sigma^2 = 1)\).

Model dimensionality when \(P=2\) and noise \(\sim N(\mu = 0, \sigma^2 = 10)\).

Model dimensionality when \(P=10\) and noise \(\sim N(\mu = 0, \sigma^2 = 0.1)\).

Model dimensionality when \(P=10\) and noise \(\sim N(\mu = 0, \sigma^2 = 1)\).

Model dimensionality when \(P=10\) and noise \(\sim N(\mu = 0, \sigma^2 = 10)\).

Model dimensionality when \(P=100\) and noise \(\sim N(\mu = 0, \sigma^2 = 0.1)\).

Model dimensionality when \(P=100\) and noise \(\sim N(\mu = 0, \sigma^2 = 1)\).

Model dimensionality when \(P=100\) and noise \(\sim N(\mu = 0, \sigma^2 = 10)\).

Model dimensionality when \(P=1000\) and noise \(\sim N(\mu = 0, \sigma^2 = 0.1)\).

Model dimensionality when \(P=1000\) and noise \(\sim N(\mu = 0, \sigma^2 = 1)\).

Model dimensionality when \(P=1000\) and noise \(\sim N(\mu = 0, \sigma^2 = 10)\).

Column

AUC when \(P=2\) and noise \(\sim N(\mu = 0, \sigma^2 = 0.1)\).

AUC when \(P=2\) and noise \(\sim N(\mu = 0, \sigma^2 = 1)\).

AUC when \(P=2\) and noise \(\sim N(\mu = 0, \sigma^2 = 10)\).

AUC when \(P=10\) and noise \(\sim N(\mu = 0, \sigma^2 = 0.1)\).

AUC when \(P=10\) and noise \(\sim N(\mu = 0, \sigma^2 = 1)\).

AUC when \(P=10\) and noise \(\sim N(\mu = 0, \sigma^2 = 10)\).

AUC when \(P=100\) and noise \(\sim N(\mu = 0, \sigma^2 = 0.1)\).

AUC when \(P=100\) and noise \(\sim N(\mu = 0, \sigma^2 = 1)\).

AUC when \(P=100\) and noise \(\sim N(\mu = 0, \sigma^2 = 10)\).

AUC when \(P=1000\) and noise \(\sim N(\mu = 0, \sigma^2 = 0.1)\).

AUC when \(P=1000\) and noise \(\sim N(\mu = 0, \sigma^2 = 1)\).

AUC when \(P=1000\) and noise \(\sim N(\mu = 0, \sigma^2 = 10)\).

Simulation 5

Column

Model dimensionality when noise \(\sim t(df = 1)\).

Column

AUC when noise \(\sim t(df = 1)\).

Real data

Column

Model dimensionality for Colon dataset.

Model dimensionality for Glioma dataset.

Model dimensionality for Leukaemia dataset.

Model dimensionality for Lung dataset.

Model dimensionality for Metastasis dataset.

Model dimensionality for MLL dataset.

Model dimensionality for SRBCT dataset.

Model dimensionality for Wine dataset.

Column

AUC for Colon dataset.

AUC for Glioma dataset.

AUC for Leukaemia dataset.

AUC for Lung dataset.

AUC for Metastasis dataset.

AUC for MLL dataset.

AUC for SRBCT dataset.

AUC for Wine dataset.

Column

Model dimensionality and AUC for Colon dataset.

Model dimensionality and AUC for Glioma dataset.

Model dimensionality and AUC for Leukaemia dataset.

Model dimensionality and AUC for Lung dataset.

Model dimensionality and AUC for Metastasis dataset.

Model dimensionality and AUC for MLL dataset.

Model dimensionality and AUC for SRBCT dataset.

Model dimensionality and AUC for Wine dataset.

Real data - Sample size

Column

Model dimensionality for Colon dataset.

Model dimensionality for Glioma dataset.

Model dimensionality for Leukaemia dataset.

Model dimensionality for Lung dataset.

Model dimensionality for Metastasis dataset.

Model dimensionality for MLL dataset.

Model dimensionality for SRBCT dataset.

Model dimensionality for Wine dataset.

Column

AUC for Colon dataset.

AUC for Glioma dataset.

AUC for Leukaemia dataset.

AUC for Lung dataset.

AUC for Metastasis dataset.

AUC for MLL dataset.

AUC for SRBCT dataset.

AUC for Wine dataset.

---
title: "More with LESS"
#author: "Machiel Visser"
#date: December 1, 2019
output: 
  flexdashboard::flex_dashboard:
    orientation: columns #rows
    vertical_layout: scroll #fill
    social: menu
    source_code: embed
---

```{r Load objects, include=FALSE}
load("FinalPlots_Simulations.RData")
load("FinalPlots_Data.RData")
load("FinalPlots_Data_SampleSize.RData")
```

# Simulation 1

## Column

### **Model dimensionality** when noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.

```{r sim1_var01_numbetas}
plot_sim1_var01_numbetas
```

### **Model dimensionality** when noise $\sim N(\mu = 0, \sigma^2 = 1)$.

```{r sim1_var1_numbetas}
plot_sim1_var1_numbetas
```

### **Model dimensionality** when noise $\sim N(\mu = 0, \sigma^2 = 10)$.

```{r sim1_var10_numbetas}
plot_sim1_var10_numbetas
```

## Column

### **AUC** when noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.

```{r sim1_var01_auc}
plot_sim1_var01_auc
```

### **AUC** when noise $\sim N(\mu = 0, \sigma^2 = 1)$.

```{r sim1_var1_auc}
plot_sim1_var1_auc
```

### **AUC** when noise $\sim N(\mu = 0, \sigma^2 = 10)$.

```{r sim1_var10_auc}
plot_sim1_var10_auc
```

# Simulation 2

## Column

### **Model dimensionality** when $P=2$.

```{r sim2_p2_numbetas}
plot_sim2_p2_numbetas
```

### **Model dimensionality** when $P=10$.

```{r sim2_p10_numbetas}
plot_sim2_p10_numbetas
```

### **Model dimensionality** when $P=100$.

```{r sim2_p100_numbetas}
plot_sim2_p100_numbetas
```

### **Model dimensionality** when $P=1000$.

```{r sim2_p1000_numbetas}
plot_sim2_p1000_numbetas
```

## Column

### **AUC** when $P=2$.

```{r sim2_p2_auc}
plot_sim2_p2_auc
```

### **AUC** when $P=10$.

```{r sim2_p10_auc}
plot_sim2_p10_auc
```

### **AUC** when $P=100$.

```{r sim2_p100_auc}
plot_sim2_p100_auc
```

### **AUC** when $P=1000$.

```{r sim2_p1000_auc}
plot_sim2_p1000_auc
```

# Simulation 3

## Column

### **Model dimensionality** when $P=2$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.

```{r sim3_p2_var01_numbetas}
plot_sim3_p2_var01_numbetas
```

### **Model dimensionality** when $P=2$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.

```{r sim3_p2_var1_numbetas}
plot_sim3_p2_var1_numbetas
```

### **Model dimensionality** when $P=2$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.

```{r sim3_p2_var10_numbetas}
plot_sim3_p2_var10_numbetas
```

### **Model dimensionality** when $P=10$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.

```{r sim3_p10_var01_numbetas}
plot_sim3_p10_var01_numbetas
```

### **Model dimensionality** when $P=10$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.

```{r sim3_p10_var1_numbetas}
plot_sim3_p10_var1_numbetas
```

### **Model dimensionality** when $P=10$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.

```{r sim3_p10_var10_numbetas}
plot_sim3_p10_var10_numbetas
```

### **Model dimensionality** when $P=100$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.

```{r sim3_p100_var01_numbetas}
plot_sim3_p100_var01_numbetas
```

### **Model dimensionality** when $P=100$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.

```{r sim3_p100_var1_numbetas}
plot_sim3_p100_var1_numbetas
```

### **Model dimensionality** when $P=100$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.

```{r sim3_p100_var10_numbetas}
plot_sim3_p100_var10_numbetas
```

### **Model dimensionality** when $P=1000$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.

```{r sim3_p1000_var01_numbetas}
plot_sim3_p1000_var01_numbetas
```

### **Model dimensionality** when $P=1000$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.

```{r sim3_p1000_var1_numbetas}
plot_sim3_p1000_var1_numbetas
```

### **Model dimensionality** when $P=1000$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.

```{r sim3_p1000_var10_numbetas}
plot_sim3_p1000_var10_numbetas
```

## Column

### **AUC** when $P=2$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.

```{r sim3_p2_var01_auc}
plot_sim3_p2_var01_auc
```

### **AUC** when $P=2$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.

```{r sim3_p2_var1_auc}
plot_sim3_p2_var1_auc
```

### **AUC** when $P=2$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.

```{r sim3_p2_var10_auc}
plot_sim3_p2_var10_auc
```

### **AUC** when $P=10$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.

```{r sim3_p10_var01_auc}
plot_sim3_p10_var01_auc
```

### **AUC** when $P=10$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.

```{r sim3_p10_var1_auc}
plot_sim3_p10_var1_auc
```

### **AUC** when $P=10$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.

```{r sim3_p10_var10_auc}
plot_sim3_p10_var10_auc
```

### **AUC** when $P=100$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.

```{r sim3_p100_var01_auc}
plot_sim3_p100_var01_auc
```

### **AUC** when $P=100$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.

```{r sim3_p100_var1_auc}
plot_sim3_p100_var1_auc
```

### **AUC** when $P=100$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.

```{r sim3_p100_var10_auc}
plot_sim3_p100_var10_auc
```

### **AUC** when $P=1000$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.

```{r sim3_p1000_var01_auc}
plot_sim3_p1000_var01_auc
```

### **AUC** when $P=1000$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.

```{r sim3_p1000_var1_auc}
plot_sim3_p1000_var1_auc
```

### **AUC** when $P=1000$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.

```{r sim3_p1000_var10_auc}
plot_sim3_p1000_var10_auc
```

# Simulation 4

## Column

### **Model dimensionality** when $P=2$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.

```{r sim4_p2_var01_numbetas}
plot_sim4_p2_var01_numbetas
```

### **Model dimensionality** when $P=2$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.

```{r sim4_p2_var1_numbetas}
plot_sim4_p2_var1_numbetas
```

### **Model dimensionality** when $P=2$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.

```{r sim4_p2_var10_numbetas}
plot_sim4_p2_var10_numbetas
```

### **Model dimensionality** when $P=10$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.

```{r sim4_p10_var01_numbetas}
plot_sim4_p10_var01_numbetas
```

### **Model dimensionality** when $P=10$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.

```{r sim4_p10_var1_numbetas}
plot_sim4_p10_var1_numbetas
```

### **Model dimensionality** when $P=10$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.

```{r sim4_p10_var10_numbetas}
plot_sim4_p10_var10_numbetas
```

### **Model dimensionality** when $P=100$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.

```{r sim4_p100_var01_numbetas}
plot_sim4_p100_var01_numbetas
```

### **Model dimensionality** when $P=100$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.

```{r sim4_p100_var1_numbetas}
plot_sim4_p100_var1_numbetas
```

### **Model dimensionality** when $P=100$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.

```{r sim4_p100_var10_numbetas}
plot_sim4_p100_var10_numbetas
```

### **Model dimensionality** when $P=1000$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.

```{r sim4_p1000_var01_numbetas}
plot_sim4_p1000_var01_numbetas
```

### **Model dimensionality** when $P=1000$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.

```{r sim4_p1000_var1_numbetas}
plot_sim4_p1000_var1_numbetas
```

### **Model dimensionality** when $P=1000$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.

```{r sim4_p1000_var10_numbetas}
plot_sim4_p1000_var10_numbetas
```

## Column

### **AUC** when $P=2$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.

```{r sim4_p2_var01_auc}
plot_sim4_p2_var01_auc
```

### **AUC** when $P=2$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.

```{r sim4_p2_var1_auc}
plot_sim4_p2_var1_auc
```

### **AUC** when $P=2$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.

```{r sim4_p2_var10_auc}
plot_sim4_p2_var10_auc
```

### **AUC** when $P=10$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.

```{r sim4_p10_var01_auc}
plot_sim4_p10_var01_auc
```

### **AUC** when $P=10$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.

```{r sim4_p10_var1_auc}
plot_sim4_p10_var1_auc
```

### **AUC** when $P=10$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.

```{r sim4_p10_var10_auc}
plot_sim4_p10_var10_auc
```

### **AUC** when $P=100$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.

```{r sim4_p100_var01_auc}
plot_sim4_p100_var01_auc
```

### **AUC** when $P=100$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.

```{r sim4_p100_var1_auc}
plot_sim4_p100_var1_auc
```

### **AUC** when $P=100$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.

```{r sim4_p100_var10_auc}
plot_sim4_p100_var10_auc
```

### **AUC** when $P=1000$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.

```{r sim4_p1000_var01_auc}
plot_sim4_p1000_var01_auc
```

### **AUC** when $P=1000$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.

```{r sim4_p1000_var1_auc}
plot_sim4_p1000_var1_auc
```

### **AUC** when $P=1000$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.

```{r sim4_p1000_var10_auc}
plot_sim4_p1000_var10_auc
```

# Simulation 5

## Column

### **Model dimensionality** when noise $\sim t(df = 1)$.

```{r sim5_numbetas}
plot_sim5_numbetas
```

## Column

### **AUC** when noise $\sim t(df = 1)$.

```{r sim5_auc}
plot_sim5_auc
```

# Real data

## Column

### **Model dimensionality** for *Colon* dataset.

```{r colon_numbetas}
plot_colon_numbetas
```

### **Model dimensionality** for *Glioma* dataset.

```{r glioma_numbetas}
plot_glioma_numbetas
```

### **Model dimensionality** for *Leukaemia* dataset.

```{r leukaemia_numbetas}
plot_leukaemia_numbetas
```

### **Model dimensionality** for *Lung* dataset.

```{r lung_numbetas}
plot_lung_numbetas
```

### **Model dimensionality** for *Metastasis* dataset.

```{r metas_numbetas}
plot_metas_numbetas
```

### **Model dimensionality** for *MLL* dataset.

```{r mll_numbetas}
plot_mll_numbetas
```

### **Model dimensionality** for *SRBCT* dataset.

```{r srbct_numbetas}
plot_srbct_numbetas
```

### **Model dimensionality** for *Wine* dataset.

```{r wine_numbetas}
plot_wine_numbetas
```

## Column

### **AUC** for *Colon* dataset.

```{r colon_auc}
plot_colon_auc
```

### **AUC** for *Glioma* dataset.

```{r glioma_auc}
plot_glioma_auc
```

### **AUC** for *Leukaemia* dataset.

```{r leukaemia_auc}
plot_leukaemia_auc
```

### **AUC** for *Lung* dataset.

```{r lung_auc}
plot_lung_auc
```

### **AUC** for *Metastasis* dataset.

```{r metas_auc}
plot_metas_auc
```

### **AUC** for *MLL* dataset.

```{r mll_auc}
plot_mll_auc
```

### **AUC** for *SRBCT* dataset.

```{r srbct_auc}
plot_srbct_auc
```

### **AUC** for *Wine* dataset.

```{r wine_auc}
plot_wine_auc
```

## Column

### **Model dimensionality** and **AUC** for *Colon* dataset.

```{r colon_2d}
plot_colon_2d
```

### **Model dimensionality** and **AUC** for *Glioma* dataset.

```{r glioma_2d}
plot_glioma_2d
```

### **Model dimensionality** and **AUC** for *Leukaemia* dataset.

```{r leukaemia_2d}
plot_leukaemia_2d
```

### **Model dimensionality** and **AUC** for *Lung* dataset.

```{r lung_2d}
plot_lung_2d
```

### **Model dimensionality** and **AUC** for *Metastasis* dataset.

```{r metas_2d}
plot_metas_2d
```

### **Model dimensionality** and **AUC** for *MLL* dataset.

```{r mll_2d}
plot_mll_2d
```

### **Model dimensionality** and **AUC** for *SRBCT* dataset.

```{r srbct_2d}
plot_srbct_2d
```

### **Model dimensionality** and **AUC** for *Wine* dataset.

```{r wine_2d}
plot_wine_2d
```

# Real data - Sample size

## Column

### **Model dimensionality** for *Colon* dataset.

```{r colon_samplesize_numbetas}
plot_colon_samplesize_numbetas
```

### **Model dimensionality** for *Glioma* dataset.

```{r glioma_samplesize_numbetas}
plot_glioma_samplesize_numbetas
```

### **Model dimensionality** for *Leukaemia* dataset.

```{r leukaemia_samplesize_numbetas}
plot_leukaemia_samplesize_numbetas
```

### **Model dimensionality** for *Lung* dataset.

```{r lung_samplesize_numbetas}
plot_lung_samplesize_numbetas
```

### **Model dimensionality** for *Metastasis* dataset.

```{r metas_samplesize_numbetas}
plot_metas_samplesize_numbetas
```

### **Model dimensionality** for *MLL* dataset.

```{r mll_samplesize_numbetas}
plot_mll_samplesize_numbetas
```

### **Model dimensionality** for *SRBCT* dataset.

```{r srbct_samplesize_numbetas}
plot_srbct_samplesize_numbetas
```

### **Model dimensionality** for *Wine* dataset.

```{r wine_samplesize_numbetas}
plot_wine_samplesize_numbetas
```

## Column

### **AUC** for *Colon* dataset.

```{r colon_samplesize_auc}
plot_colon_samplesize_auc
```

### **AUC** for *Glioma* dataset.

```{r glioma_samplesize_auc}
plot_glioma_samplesize_auc
```

### **AUC** for *Leukaemia* dataset.

```{r leukaemia_samplesize_auc}
plot_leukaemia_samplesize_auc
```

### **AUC** for *Lung* dataset.

```{r lung_samplesize_auc}
plot_lung_samplesize_auc
```

### **AUC** for *Metastasis* dataset.

```{r metas_samplesize_auc}
plot_metas_samplesize_auc
```

### **AUC** for *MLL* dataset.

```{r mll_samplesize_auc}
plot_mll_samplesize_auc
```

### **AUC** for *SRBCT* dataset.

```{r srbct_samplesize_auc}
plot_srbct_samplesize_auc
```

### **AUC** for *Wine* dataset.

```{r wine_samplesize_auc}
plot_wine_samplesize_auc
```